Racial Gradients of Ambient Air Pollution Exposure in Hamilton, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental justice research in the United States has coalesced around the notion that visible-minority status, along with socioeconomic position (SEP), conditions exposure to environmental health hazards. In the context of long-standing debates over Canada–USA urban differences, we address the question of whether racial gradients exist in air pollution across Hamilton, Canada. Monitored air quality data are spatially interpolated with a kriging algorithm. These interpolated exposures are statistically correlated with 1996 data at the census tract scale, with the aid of multivariate and spatial techniques. The proportion of Latin-Americans in a census tract is positively associated with pollution exposure, even after control for many SEP variables. In contrast, Asian-Canadians are negatively associated with air pollution, and Black-Canadians show no clear correlation at all. Thus, the faces of environmental racism in Canada seem more varied and nuanced than in the USA. Given the immigrant basis of visible minorities in Canada, we argue that Hamilton (and the Canadian city generally) may represent new dimensions of environmental racism driven by economic status at time of entry. In drawing on similar findings in the USA and the United Kingdom, the authors conclude that environmental racism appears present in all jurisdictions, but that the nature and extent of disproportionate exposure differ between countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it